Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area

Nhu, Viet-Ha; Hoang, Nhat‐Duc; Nguyen, Hieu; Ngo, Phuong Thao Thi; Bui, Tinh Thanh; Hoa, Pham Viet; Samui, Pijush; Bui, Dieu Tien

doi:10.1016/j.catena.2020.104458

Cited by 113 publications

(51 citation statements)

References 56 publications

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“…After selecting the most important conditioning factors, we modelled and evaluated the data of, respectively, the training and validation datasets. We selected optimal parameters, including the number of iterations (10) and number of seeds (20), by trial-and-error according to the RMSE and AUC. Results are shown in Table 2 and Figure 4.…”

Section: Modeling Process and Evaluationsmentioning

confidence: 99%

“…Razavizadeh et al [8] concluded that FR outperformed the statistical index (SI) and weights of evidence (WOF) for landslide prediction, and Juliev et al [9] illustrated the supremacy of SI over the certainty factor (CF) and FR. Van Finally, Dao et al [3] and Nhu et al [10] explored the potential application of deep learning ANN for landslide modeling and prediction, and showed its superiority over several other machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

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Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran

Nhu

Shirzadi

Shahabi

et al. 2020

Forests

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We generated high-quality shallow landslide susceptibility maps for Bijar County, Kurdistan Province, Iran, using Random Forest (RAF), an ensemble computational intelligence method and three meta classifiers—Bagging (BA, BA-RAF), Random Subspace (RS, RS-RAF), and Rotation Forest (RF, RF-RAF). Modeling and validation were done on 111 shallow landslide locations using 20 conditioning factors tested by the Information Gain Ratio (IGR) technique. We assessed model performance with statistically based indexes, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). All four machine learning models that we tested yielded excellent goodness-of-fit and prediction accuracy, but the RF-RAF ensemble model (AUC = 0.936) outperformed the BA-RAF, RS-RAF (AUC = 0.907), and RAF (AUC = 0.812) models. The results also show that the Random Forest model significantly improved the predictive capability of the RAF-based classifier and, therefore, can be considered as a useful and an effective tool in regional shallow landslide susceptibility mapping.

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Section: Modeling Process and Evaluationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran

Nhu

Shirzadi

Shahabi

et al. 2020

Forests

Self Cite

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“…The ROC curve is the standard and most common method used to evaluate the performance of the landslide hazard prediction model [59,60]. It is plotted by the "Sensitivity" against the "1-Specificity" in statistics.…”

Section: Model Evaluationmentioning

confidence: 99%

Hazard Mapping of the Rainfall–Landslides Disaster Chain Based on GeoDetector and Bayesian Network Models in Shuicheng County, China

Rong

Han

et al. 2020

Water

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Landslides are among the most frequent natural hazards in the world. Rainfall is an important triggering factor for landslides and is responsible for topples, slides, and debris flows—three of the most important types of landslides. However, several previous relevant research studies covered general landslides and neglected the rainfall–topples–slides–debris flows disaster chain. Since landslide hazard mapping (LHM) is a critical tool for disaster prevention and mitigation, this study aimed to build a GeoDetector and Bayesian network (BN) model framework for LHM in Shuicheng County, China, to address these geohazards. The GeoDetector model will be used to screen factors, eliminate redundant information, and discuss the interaction between elements, while the BN model will be used for constructing a causality disaster chain network to determine the probability and risk level of the three types of landslides. The practicability of the BN model was confirmed by error rate and scoring rules validation. The prediction accuracy results were tested using overall accuracy, Matthews correlation coefficient, relative operating characteristics curve, and seed cell area index. The proposed framework is demonstrated to be sufficiently accurate to construct the complex LHM. In summary, the combination of the GeoDetector and BN model is very promising for spatial prediction of landslides.

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“…In this article, PyCharm was selected as a compiler to code the deep learning algorithm through Python. After previous experience and trial-and-error [29,78], the parameters of DNN and SSL-DNN models were determined, as shown in Table 3. Moreover, Figure 9 reflects the variation of accuracy and loss as the iteration progresses.…”

Section: Landslides Susceptibility Assessmentmentioning

confidence: 99%

“…The latest research has revealed that DNN processed high learning potential in landslide susceptibility assessment with different sampling strategies [28]. The DNN models with multiple optimization algorithms such as stochastic gradient descent (SGD), root mean square propagation (RMSProp), and adaptive moment optimization (Adam) have been compared with traditional machine learning models [29], and their excellent performance and applicability have been confirmed for landslide susceptibility.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Landslide Susceptibility Combining Deep Learning with Semi-Supervised Learning in Jiaohe County, Jilin Province, China

et al. 2020

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Accurate and timely landslide susceptibility mapping (LSM) is essential to effectively reduce the risk of landslide. In recent years, deep learning has been successfully applied to landslide susceptibility assessment due to the strong ability of fitting. However, in actual applications, the number of labeled samples is usually not sufficient for the training component. In this paper, a deep neural network model based on semi-supervised learning (SSL-DNN) for landslide susceptibility is proposed, which makes full use of a large number of spatial information (unlabeled data) with limited labeled data in the region to train the mode. Taking Jiaohe County in Jilin Province, China as an example, the landslide inventory from 2000 to 2017 was collected and 12 metrological, geographical, and human explanatory factors were compiled. Meanwhile, supervised models such as deep neural network (DNN), support vector machine (SVM), and logistic regression (LR) were implemented for comparison. Then, the landslide susceptibility was plotted and a series of evaluation tools such as class accuracy, predictive rate curves (AUC), and information gain ratio (IGR) were calculated to compare the prediction of models and factors. Experimental results indicate that the proposed SSL-DNN model (AUC = 0.898) outperformed all the comparison models. Therefore, semi-supervised deep learning could be considered as a potential approach for LSM.

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Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area

Cited by 113 publications

References 56 publications

Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran

Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran

Hazard Mapping of the Rainfall–Landslides Disaster Chain Based on GeoDetector and Bayesian Network Models in Shuicheng County, China

Assessment of Landslide Susceptibility Combining Deep Learning with Semi-Supervised Learning in Jiaohe County, Jilin Province, China

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